Snowflake DAA-C01 : SnowPro Advanced: Data Analyst Certification Exam

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Snowflake DAA-C01 Exam Syllabus Topics:

Snowflake SnowPro Advanced: Data Analyst Certification Sample Questions:

1. A Data Analyst is working with three tables:

Which query would return a list of all brokers, a count of the customers each broker has. and the total order amount of their customers (as shown below)?

A)

B)

C)

D)

2. A Data Analyst is working with a table that has 1 record per day, with sales information. Which window function would calculate a 7-day moving average of sales, where SALES_DATE represents the date column?

A) AVG(SALES) OVER (ORDER BY SALES_DATE ROWS BETWEEN 7 PRECEDING AND
CURRENT ROW)
B) AVG(SALES) OVER (ORDER BY SALES_DATE ROWS BETWEEN 6 PRECEDING AND
CURRENT ROW)
C) SUM(SALES) OVER (ORDER BY SALES_DATE ROWS BETWEEN 7 PRECEDING AND
CURRENT ROW)
D) SUM(SALES) OVER (ORDER BY SALES_DATE ROWS BETWEEN 6 PRECEDING AND
CURRENT ROW)

3. A Data Analyst has been asked to predict sales revenue through the end of the year. Which function will provide this information?

A) REGR_SLOPE
B) VARIANCE
C) CORR
D) COVAR_SAMP

4. What functionalities are available when a Snowflake worksheet is shared with other users? (Select TWO).

A) If the worksheet is being edited, the collaborators will be able to see these edits in real-time.
B) Whenever a user with permissions runs a worksheet, the existing version history of the worksheet will be overwritten.
C) If multiple users edit and run a shared worksheet at the same time, each run of the worksheet will create a new version.
D) Collaborators can share the worksheet across Snowflake accounts.
E) Users with edit permissions can view past versions of the worksheet.

5. A large, complicated query is used to generate a data set for a report on the most recent month. It is taking longer than expected. A review of the Query Profile shows excessive spilling. How can the performance of the query be improved WITHOUT increasing costs?

A) Run the query against zero-copy clones of the source tables to avoid contention with other queries.
B) Split the query into multiple steps, replacing Common Table Expressions (CTEs) with temporary tables to process the data in smaller batches.
C) Change the source tables into external tables to establish and take advantage of custom partitioning.
D) Create a materialized view clustered on a date column, on the table that is causing the spilling.

Solutions: